Fix QwenImage txt_seq_lens handling

[kashif]

What does this PR do?

• Removes the redundant txt_seq_lens plumbing from all QwenImage pipelines and modular steps; the transformer now infers text length from encoder inputs/masks and validates optional overrides.
• Builds a lightweight broadcastable attention mask from encoder_hidden_states_mask inside the double-stream attention, avoiding full seq_len² masks while keeping padding tokens masked.
• Adjusts QwenImage Transformer/ControlNet RoPE to take a single text length and documents the fallback behavior.
• Adds regression tests to ensure short txt_seq_lens values and encoder masks are handled safely.

Fixes #12344

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[dxqb]

just a few comments, not a full review:

• there is some overlap with Fix qwen encoder hidden states mask #12655
• this code has the same issue mentioned in Fix qwen encoder hidden states mask #12655 (expecting boolean semantics in a FloatTensor - but float attention masks are interpreted differently)
• Could you clarify what the purpose of this PR is?
  If the purpose is to remove the txt_seq_lens parameters, and infer the sequence lengths from the attention mask: why is it still a parameter of the transformer model?
  If the purpose is towards passing sequence lengths to the attention dispatch (see Qwen Image: txt_seq_lens is redundant and not used #12344 (comment)), the sequence lengths for each batch sample must be inferred from the mask and passed to the transformer blocks, not only the max sequence length across all batch samples for RoPE

[dxqb]

This only works if the attention mask is in the form of [True, True, True, ..., False, False, False]. While this is the case in the most common use case of text attention masks, it doesn't have to be the case.

If the mask is [True, False, True, False, True, False], self.pos_embed receives an incorrect sequence length

[kashif]

thanks @dxqb the idea was to remove txt_seq_lens all together and work with any mask pattern